Adding levels to MultiIndex, removing without losing
pandas, python
Solution
In the current version (0.17.1) it is possible to
df.set_index(column_to_add, append=True, inplace=True)
and
df.reset_index(level=column_to_remove_from_index).
This comes along with a substantial speedup versus resetting n columns and then adding n+1 to the index.
Problem
Let's assume I have a `DataFrame` df with a MultiIndex and it has the level L. Is there a way to remove L from the index and add it again? `df = df.index.drop('L')` removes L completely from the DataFrame ( unlike `df= df.reset_index()` which has a drop argument). I could of course do `df = df.reset_index().set_index(everything_but_L, inplace=True)`. Now, let us assume the index contains everything but L, and I want to add L. `df.index.insert(0, df.L)` doesn't work. Again, I could of course call `df= df.reset_index().set_index(everything_including_L, inplace=True)` but it doesn't feel right. Why do I need this? Since indices need not be unique, it can occur that I want to add a new column so the index becomes unique. Dropping may be useful in situations where after splitting data one level of the index does not contain any information anymore (say my index is A,B and I operate on a df with A=x but I do not want to lose A which would occur with index.droplevel('A')).